Building Explainable Artificial Intelligence Systems

By Mark G. Core, H. Chad Lane, Michael van Lent, Dave Gomboc, Steve Solomon, Milton Rosenberg. 2006

148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021, background): “Symbolic or semi-symbolic architectures naturally produce justiﬁcations as a sequence of formulas in some formal language (Lane et al., 2005; Core et al., 2006; Lomas et al., 2012; Johnson; Liang, 2016; Malinowski and Fritz, 2014).”
Generating Visual Explanations (2016, background): “Automatic reasoning and explanation has a long and rich history within the artificial intelligence community [1,13,14,15,16,17,18,19].”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…2018; Biran and Cotton, 2017; Brézillon, 1994; Brézillon and Pomerol, 1997; Buchanan and Shortliffe, 1984; Chakraborty et al., 2017; Clancey, 1986; Core et al., 2006; Craven and Shavlik, 1996; Doran, Shulz, and Besold, 2017; Doshi-Velez and Kim, 2017ab; Doyle, Tsymbal, and Cunningham, 2003;…”
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence (2018, background): “, medical [31] and feedback for teaching programs [19, 32, 9]).”
What do we need to build explainable AI systems for the medical domain? (2017, background): “…of explainability is as old as AI and maybe the result of AI itself: whilst AI approaches demonstrate impressive practical success in many diﬀerent application domains, their eﬀectiveness is still limited by their inability to ”explain” their decisions in an under-standable way (Core et al., 2006).”
Grounding Visual Explanations (2018, methodology): “Early textual explanation models are applied to medical images [25] and developed as a feedback for teaching programs [12,27,3].”
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (2016, background): “More recently, [17] developed a deep network to generate natural language justiﬁcations of a ﬁne-grained object classiﬁer.”
Socratic Learning (2016, background): “We provide theoretical guarantees in Theorem 1 for this improvement.”
Design and Evaluation of Explainable BDI Agents (2010, background): “The developers notice that simulations differ in their ’explanation-friendliness’ [1].”
Novelty Detection and Learning from Extremely Weak Supervision (2019, background): “Moreover, the most powerful state-of-the-art approaches such as deep neural networks and SVM suffer from lack of interpretability [8, 9, 10].”
The role of emotion in self-explanations by cognitive agents (2017, background): “XAI systems often use question lists, allowing the user a limited set of questions to ask [7, 34].”
Explainable artificial intelligence: an analytical review (2021, background): “…especially, DL) become more wide spread and intertwined with human-centric applications, and algorithmic decisions become more consequential to individuals and society, attention has shifted back from accuracy to explainability (Angelov & Soares, 2020; Core et al., 2006; Pedreschi et al., 2019).”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Based in these early studies, [123, 124, 125, 126] proposed a modular architecture for explaining the behavior of simulated entities in military simulations.”
Do You Get It? User-Evaluated Explainable BDI Agents (2010, background): “The second XAI system can provide explanations in terms of an agent’s goals, but only if those are represented as such in the simulation, which is often not the case [13].”
An Interpretable Generative Model for Handwritten Digits Synthesis (2019, background): “Explanable machine learning (XML) [3, 4] and explainable Artificial Intelligence (XAI) [5, 6] have attracted a lot of attention recently.”
Key challenges for delivering clinical impact with artificial intelligence (2019, background): “While AI approaches in medicine have yielded some impressive practical successes to date, their effectiveness is limited by their inability to ‘ explain ’ their decision-making in an understandable way [87].”